Exploring the potential of memristors for quantum computation and artificial intelligence – News

Yann Beilliard and Dominique Drouin, teachers to Department of Electrical Engineering and Computer Engineering of the Faculty of Engineering, and members of the Quantum Institute in UdeS.

Photo: Michel Caron – UdeS

Although the term biomimicry first entered Le Robert in 1975, the process has been in use much longer. “Learn from nature, you will find the future there,” Leonardo da Vinci said.

The future is for many researchers at the Institut quantique (IQ) the quantum computer. In addition to its architecture, algorithms and error correction, this research tool with enormous potential will also require the participation of engineers who will ensure its control. This is a colossal challenge and there is still a lot of work to be done as maintaining quantum states requires cryogenic temperatures. It is therefore necessary to develop electronics that can operate at these inhospitable temperatures.

To create the quantum computer, electronics must be developed that are capable of operating at cryogenic temperatures.

And to do this, a research team from the Department of Quantum (IQ) and the Interdisciplinary Institute for Technological Innovation (3IT) is interested in memristors (contraction of memory and of resistance). This electronic component, theoretically predicted by Professor Leon Chua of Berkeley in 1971, was not realized until about forty years later. It is a nanoscopic electronic component whose resistance can be changed as desired. This property makes memristors very promising candidates for the realization of artificial synapses within the framework of circuits optimized for artificial intelligence (AI).

What we are trying to do requires the combination of expertise found in 3IT and IQ. We orient our research projects at the interface between artificial intelligence, budding nanoelectronics and quantum science; you need all three at the same time.

Yann Beilliard, Professor of Engineering and Member of IQ

At 3IT, we are developing resistive memories, also called “memristors”, the researcher continues. These are new nanocomponents that make it possible to develop high-performance electronic circuits specifically for AI. So three years ago the idea arose: why not use these technologies for quantum computer scaling Specifically, it is a matter of contributing to the automatic control of qubits using AI, whether one chooses quantum dots on silicon or even superconducting circuits.Classic electronics are needed to control the quantum chips in the cryostat.If we want to scale quantum technology by thousands or even millions of qubits, we will have to automate the processes using AI and use classical control electronics, which is very robust.

The research team identifies artificial intelligence as a promising way to automate certain control procedures for quantum systems, ranging from reading qubits to tomography, including the state of qubits and controlling quantum units on silicon.

You need high-performance computer hardware to make the artificial intelligence work, to avoid heating up the cryostat. You also need optimized electronics for everything to work efficiently.

“Our team is collaborating with Roger Melko, a professor at the University of Waterloo and a researcher at the Perimeter Institute, and Stefanie Czischek, a Ph.D. application of AI to quantum, explains Dominique Drouin, professor at the Faculty of Engineering. This research project is funded by the IQ call for projects, and when we needed experimental data to train neural networks for quantum dots on silicon, we relied on research work by Sophie Rochette and Julien Camirand Lemyre as they did their doctorate in the group of Professor Michel Pioro-Ladrière In addition to working at the interface between several disciplines and exploiting the resources of 3IT and IQ, we relied on a collaborative approach. which is a step closer to automating certain procedures. “

The next one works

Yann Beilliard reminds us of the temperature challenge: “We need cryogenic resistive memories, specially adapted to the operating constraints of quantum systems in order to implement high-performance AI-based control methods directly in the cryostat. On the other hand, all the so far developed memories are made to be used by The next step is therefore to design memristors specially adapted to cryogenic conditions to unlock all applications.It will therefore be necessary to work with both the materials and the architecture of the components, especially by using superconducting materials, the first within memristors. “

This is where nature can have its limitations, and the resources of 3IT, IQ, and partners come into their own.

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